Towards the Automatic and Schedule-Aware Alerting of Internetwork Time Series
Entity
UAM. Departamento de Tecnología Electrónica y de las ComunicacionesPublisher
Institute of Electrical and Electronics Engineers Inc. (IEEE)Date
2021-04-15Citation
10.1109/ACCESS.2021.3073598
IEEE Access 9 (2021): 61346-61358
ISSN
2169-3536 (print)DOI
10.1109/ACCESS.2021.3073598Project
Gobierno de España. PID2019-104451RB-C21Editor's Version
https://doi.org/10.1109/ACCESS.2021.3073598Subjects
Monitoring systems; Alerting module; Internetwork time series; LSTM networks; Network managers schedule; TelecomunicacionesAbstract
A common factor of every network monitoring system is an alerting module for time series. This
module aims at triggering a warning when any type of abnormal behavior is detected in the patterns of a time
series. Such a search for anomalies can be carried out by network managers as a supervised task such that the
thresholds for considering a measurement as an anomaly are set following a manual process. Alternatively,
we focus on how to translate such a task to an unsupervised one, thus alleviating network managers’
dedication. To this end, we have developed, based on the experience of monitoring dozens of networks,
a player of real anomalies. Thus, by recreating real issues, the alerting systems’ parametrization can be carried
out without supervision. Additionally, as a novelty, we propose to consider the network managers’ workforce
as a significant parameter to configure the thresholds of the alerting module—essentially, avoiding triggering
alarms that will hardly receive attention. Then, we propose to measure and rank alarms by relevance, and
relate them to the time to be solved for constructing, eventually, automatic schedules for the members of the
staff—according to their time availability. Finally, all these proposals have been put into practice in various
deployments of monitoring systems on networks in operation, which gives us evidence of its usefulness and
low demand for resources.
Files in this item
Google Scholar:Perdices Burrero, Daniel
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García Dorado, José Luis
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Ramos, Javier
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De Pool, Rodrigo
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Aracil, Javier
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